# Copyright 1999-2020 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import pickle
from collections import OrderedDict, defaultdict

import numpy as np

from ....serialize import ValueType, DictField, KeyField, ListField
from ....operands import MergeDictOperand, OutputType
from .... import opcodes as OperandDef
from ....context import get_context, RunningMode
from .start_tracker import StartTracker
from .dmatrix import ToDMatrix


def _on_serialize_evals(evals_val):
    if evals_val is None:
        return None
    return [list(x) for x in evals_val]


class XGBTrain(MergeDictOperand):
    _op_type_ = OperandDef.XGBOOST_TRAIN

    _params = DictField('params', key_type=ValueType.string)
    _dtrain = KeyField('dtrain')
    _evals = ListField('evals', on_serialize=_on_serialize_evals)
    _kwargs = DictField('kwargs', key_type=ValueType.string)
    _tracker = KeyField('tracker')

    def __init__(self, params=None, dtrain=None, evals=None, kwargs=None,
                 tracker=None, gpu=None, **kw):
        super().__init__(_params=params, _dtrain=dtrain, _evals=evals, _kwargs=kwargs,
                         _tracker=tracker, _gpu=gpu, **kw)
        if self.output_types is None:
            self.output_types = [OutputType.object]

    @property
    def params(self):
        return self._params

    @property
    def dtrain(self):
        return self._dtrain

    @property
    def evals(self):
        return self._evals

    @property
    def kwargs(self):
        return self._kwargs

    @property
    def tracker(self):
        return self._tracker

    def _set_inputs(self, inputs):
        super()._set_inputs(inputs)
        self._dtrain = self._inputs[0]
        rest = self._inputs[1:]
        if self._tracker is not None:
            self._tracker = self._inputs[-1]
            rest = rest[:-1]
        if self._evals is not None:
            evals_dict = OrderedDict(self._evals)
            new_evals_dict = OrderedDict()
            for new_key, val in zip(rest, evals_dict.values()):
                new_evals_dict[new_key] = val
            self._evals = list(new_evals_dict.items())

    def __call__(self):
        inputs = [self._dtrain]
        if self._evals is not None:
            inputs.extend(e[0] for e in self._evals)
        return self.new_tileable(inputs)

    @staticmethod
    def _get_dmatrix_chunks_workers(ctx, dmatrix):
        # dmatrix_chunk.inputs is concat, and concat's input is the coallocated chunks
        metas = ctx.get_chunk_metas([c.inputs[0].inputs[0].key for c in dmatrix.chunks])
        return [m.workers[0] for m in metas]

    @staticmethod
    def _get_dmatrix_worker_to_chunk(dmatrix, workers, ctx):
        worker_to_chunk = dict()
        expect_workers = set(workers)
        workers = XGBTrain._get_dmatrix_chunks_workers(ctx, dmatrix)
        for w, c in zip(workers, dmatrix.chunks):
            if w in expect_workers:
                worker_to_chunk[w] = c
        return worker_to_chunk

    @classmethod
    def tile(cls, op):
        ctx = get_context()
        if ctx.running_mode != RunningMode.distributed:
            assert all(len(inp.chunks) == 1 for inp in op.inputs)

            chunk_op = op.copy().reset_key()
            out_chunk = chunk_op.new_chunk([inp.chunks[0] for inp in op.inputs],
                                           shape=(1,), index=(0,))
            new_op = op.copy()
            return new_op.new_tileables(op.inputs, chunks=[out_chunk], nsplits=((1,),))
        else:
            inp = op.inputs[0]
            in_chunks = inp.chunks
            workers = cls._get_dmatrix_chunks_workers(ctx, inp)
            n_chunk = len(in_chunks)
            tracker_chunk = StartTracker(n_workers=n_chunk, pure_depends=[True] * n_chunk)\
                .new_chunk(in_chunks, shape=())
            out_chunks = []
            worker_to_evals = defaultdict(list)
            if op.evals is not None:
                for dm, ev in op.evals:
                    worker_to_chunk = cls._get_dmatrix_worker_to_chunk(dm, workers, ctx)
                    for worker, chunk in worker_to_chunk.items():
                        worker_to_evals[worker].append((chunk, ev))
            for in_chunk, worker in zip(in_chunks, workers):
                chunk_op = op.copy().reset_key()
                chunk_op._expect_worker = worker
                chunk_op._tracker = tracker_chunk
                chunk_evals = list(worker_to_evals.get(worker, list()))
                chunk_op._evals = chunk_evals
                input_chunks = [in_chunk] + [pair[0] for pair in chunk_evals] + [tracker_chunk]
                out_chunk = chunk_op.new_chunk(input_chunks, shape=(np.nan,),
                                               index=in_chunk.index[:1])
                out_chunks.append(out_chunk)

            new_op = op.copy()
            return new_op.new_tileables(op.inputs, chunks=out_chunks,
                                        nsplits=((np.nan for _ in out_chunks),))

    @classmethod
    def execute(cls, ctx, op):
        if op.merge:
            return super().execute(ctx, op)

        from xgboost import train, rabit

        dtrain = ToDMatrix.get_xgb_dmatrix(ctx[op.dtrain.key])
        evals = tuple()
        if op.evals is not None:
            eval_dmatrices = [ToDMatrix.get_xgb_dmatrix(ctx[t[0].key]) for t in op.evals]
            evals = tuple((m, ev[1]) for m, ev in zip(eval_dmatrices, op.evals))
        params = op.params
        params['nthread'] = ctx.get_ncores() or -1

        if op.tracker is None:
            # non distributed
            local_history = dict()
            kwargs = dict() if op.kwargs is None else op.kwargs
            bst = train(params, dtrain, evals=evals,
                        evals_result=local_history, **kwargs)
            ctx[op.outputs[0].key] = {'booster': pickle.dumps(bst), 'history': local_history}
        else:
            # distributed
            rabit_args = ctx[op.tracker.key]
            rabit.init(rabit_args)
            try:
                local_history = dict()
                bst = train(params, dtrain, evals=evals, evals_result=local_history,
                            **op.kwargs)
                ret = {'booster': pickle.dumps(bst), 'history': local_history}
                if rabit.get_rank() != 0:
                    ret = {}
                ctx[op.outputs[0].key] = ret
            finally:
                rabit.finalize()


def train(params, dtrain, evals=(), **kwargs):
    """
    Train XGBoost model in Mars manner.

    Parameters
    ----------
    Parameters are the same as `xgboost.train`.

    Returns
    -------
    results: Booster
    """

    evals_result = kwargs.pop('evals_result', dict())
    session = kwargs.pop('session', None)
    run_kwargs = kwargs.pop('run_kwargs', dict())
    op = XGBTrain(params=params, dtrain=dtrain, evals=evals, kwargs=kwargs)
    t = op()
    ret = t.execute(session=session, **run_kwargs).fetch(session=session)
    evals_result.update(ret['history'])
    bst = pickle.loads(ret['booster'])
    num_class = params.get('num_class')
    if num_class:
        bst.set_attr(num_class=str(num_class))
    return bst
